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LearningCertificate · Udemy

Practical AI with Python and Reinforcement Learning

An application-focused course on creating intelligent agents that learn through trial and error within dynamic environments. Explores Deep Q-Learning, SARSA, and the Cross-Entropy method to solve complex decision-making problems.

Instructor

Jose Portilla

Duration

26.5 hours

Issued

Pierian Training

The certificate

Practical AI with Python and Reinforcement Learning

In progress — no credential yet

View the course

What it covered — 15 modules

  1. 01

    Course Overview

    Welcome to the Course · Course Curriculum Overview

    0/2 lectures
  2. 02

    Course Set-Up and Installation Procedures

    Installation and Environment Setup · Python and Library Requirements

    0/2 lectures
  3. 03

    Numpy Basics Overview

    Numpy Arrays · Numpy Operations

    0/2 lectures
  4. 04

    Matplotlib and Visualization Overview

    Data Visualization with Matplotlib · Basic Plotting Techniques

    0/2 lectures
  5. 05

    Machine Learning, Deep Learning, and Reinforcement Learning

    Theory and Differences between ML, DL, and RL

    0/1 lectures
  6. 06

    Pandas and Scikit-Learn Crash Course

    Data Analysis with Pandas · Machine Learning with Scikit-Learn Basics

    0/2 lectures
  7. 07

    Artificial Neural Network and TensorFlow Basics

    Perceptrons and Multi-Layer Perceptrons · TensorFlow and Keras Basics · Building your first ANN

    0/3 lectures
  8. 08

    Convolutional Neural Networks with TensorFlow

    CNN Theory · Image Processing and Convolutions · Implementing CNNs for Image Recognition

    0/3 lectures
  9. 09

    Reinforcement Learning - Core Concepts

    Agent, Environment, and Reward · The Markov Decision Process (MDP) · The Bellman Equation

    0/3 lectures
  10. 10

    OpenAI Gym Overview

    Introduction to OpenAI Gym Environments · Creating and Interacting with Environments

    0/2 lectures
  11. 11

    Classical Q-Learning

    Tabular Q-Learning Theory · Q-Table Implementation · SARSA Algorithm

    0/3 lectures
  12. 12

    Deep Q-Learning

    Deep Q-Network (DQN) Theory · Experience Replay · Implementing DQN in Python

    0/4 lectures
  13. 13

    Deep Q-Learning on Images

    Processing Pixels as Input · CNN + DQN Integration · Training Agents to play Atari Games

    0/3 lectures
  14. 14

    Creating Custom OpenAI Gym Environments

    Designing Custom Environments · Defining Reward Systems for Real-World Problems

    0/2 lectures
  15. 15

    Additional RL Methods

    Cross Entropy Method · Policy Gradients (Early Bird Content)

    0/2 lectures

Toolkit from this course

TensorFlowKerasOpenAI Gym (Gymnasium)NumPyMatplotlib

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